Sexual Dimorphism or Statistical Overlap? Diagnostic Evaluation of the Gonial Angle for Gender Determination - An Anthropometric Approach

Sexual Dimorphism or Statistical Overlap? 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Diagnostic Evaluation of the Gonial Angle for Gender Determination - An Anthropometric Approach Binigha M, Abirami Arthanari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9175757/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Sex estimation is a critical step in forensic human identification. The mandible, owing to its structural resilience and morphological variation between sexes, has been widely studied for gender determination. Among its parameters, the gonial angle has been proposed as a potential radiomorphometric indicator; however, population-specific validation remains limited in the Tamil Nadu population. Aim To evaluate the reliability and predictive accuracy of the gonial angle for gender determination using digital orthopantomograms in a South Indian population. Materials and Methods A retrospective cross-sectional study was conducted on 158 digital panoramic radiographs comprising 79 males and 79 females aged 20–30 years. Bilateral gonial angles were measured using Planmeca Romexis software. Gender differences were assessed using the Mann–Whitney U test. Diagnostic performance was evaluated using Receiver Operating Characteristic (ROC) curve analysis, and binary logistic regression analysis was performed to assess predictive accuracy. Statistical significance was set at p < 0.05. Results The mean gonial angle was 127.78° ± 9.15 in males and 125.42° ± 6.21 in females. The difference was not statistically significant (p = 0.059). ROC analysis demonstrated poor discriminatory ability (AUC = 0.413; 95% CI: 0.324–0.502). The optimal cut-off value (125.48°) yielded sensitivity of 50.6% and specificity of 40.5%. Logistic regression analysis did not show gonial angle to be a significant predictor of gender (p = 0.093). Overall classification accuracy was 56.3%. Conclusion Within the studied Tamil Nadu population, gonial angle alone demonstrated limited predictive value for gender determination. It may serve as an adjunctive parameter when combined with other mandibular measurements in multivariate models. Gonial angle Gender determination Orthopantomogram Sexual dimorphism Forensic odontology Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Sex determination forms the cornerstone of forensic identification and significantly narrows the biological profile of unidentified remains(Lynch, Heathfield and Budowle, 2025 ). In scenarios involving mass disasters, explosions, or advanced decomposition, skeletal structures often represent the primary source of identification. International forensic identification protocols, including those endorsed by Interpol, emphasize the value of durable skeletal markers exhibiting sexual dimorphism (Koenig et al., 2024 ). The mandible is the strongest and most resistant bone of the facial skeleton and frequently survives environmental and mechanical insults (Koenig et al., 2024 ; Adnan et al., 2025 ). Morphological differences between males and females arise due to variations in growth patterns, hormonal influences, and masticatory loading. Several mandibular parameters including ramus height, bigonial width, body length, and gonial angle have been investigated for their forensic relevance (Kazama et al., 2025 ). The gonial angle, formed by the intersection of the inferior border of the mandibular body and the posterior border of the ramus, is easily identifiable on panoramic radiographs. Despite numerous investigations, findings regarding its sexual dimorphism remain inconsistent across populations. Ethnic variability, genetic diversity, dietary habits, and biomechanical forces may contribute to these discrepancies(Farrera et al., 2025 ; Pinto et al., 2025 ) . Digital orthopantomography provides a non-invasive and accessible method for assessing mandibular morphology (Craig, Powell and Price, 2013 ). While three-dimensional imaging offers enhanced accuracy, panoramic radiography remains widely used in clinical and forensic settings due to feasibility and availability (Sisman et al., 2012 ). Although many studies report statistically significant gender differences in gonial angle, fewer have evaluated its predictive accuracy using analytical models such as ROC curves and logistic regression(Ndjonko et al., 2026 ). Therefore, this study aimed to assess the reliability and predictive accuracy of gonial angle for gender determination in individuals aged 20–30 years from south indian population using digital panoramic radiographs. MATERIALS AND METHODS Study design and setting The present retrospective cross-sectional study was conducted in the Department of Forensic Odontology. Digital panoramic radiographs were obtained from the archives of the Department of Oral Medicine and Radiology, Saveetha Dental College and Hospital. Institutional ethical clearance was obtained prior to commencement of the study (IHEC/SDC/UG-2289/26/FORENSIC/081). Study sample The study comprised a total of 158 digital orthopantomograms (OPGs), including 79 males and 79 females. The age of the subjects ranged from 20 to 30 years. The age group was selected to minimize age-related morphological changes in the mandible and to ensure skeletal maturity. Radiographs were selected using a non-probability consecutive sampling method from archived records. The sex of each individual was confirmed from institutional records prior to inclusion. Inclusion criteria Radiographs were included in the study if they met the following criteria: Radiographs of individuals aged between 20 and 30 years Radiographs with documented sex High-quality digital panoramic radiographs with clear visualization of bilateral mandibular borders Radiographs free from distortion affecting the gonial region Exclusion criteria Radiographs were excluded under the following conditions: Presence of pathological lesions involving the mandible History or radiographic evidence of mandibular fractures Developmental anomalies of the mandible Radiographs exhibiting positioning errors, magnification distortion, or unclear visualization of the gonial region Radiographs with abnormalities that interfered with accurate identification of anatomical landmarks Radiographic procedure All the panoramic radiographic images were acquired using Planmeca ProMax® 3D Max panoramic machine (Planmeca Oy, Helsinki, Finland). Exposure parameters were ranging from 64–68 kVp, 6.3–10 mA, and 0.19 s according to the patient’s age and size. The radiographs had been taken following standard exposure protocols with patients positioned according to manufacturer guidelines to ensure reproducibility and minimize distortion. Only radiographs meeting institutional quality assurance standards were considered for analysis. Measurement of gonial angle The gonial angle was measured bilaterally using Planmeca Romexis software (Planmeca Oy, Helsinki, Finland), which allows digital tracing and angular measurement with precision tools. Landmark identification The gonial angle was defined as the angle formed by the intersection of: A line tangent to the inferior border of the mandibular body A line tangent to the posterior border of the ascending ramus and condylar process which shown in Figs. 1 & 2 Measurement procedure The digital image was opened in the software measurement interface. The first tangent line was drawn along the lower border of the mandible, extending posteriorly. The second tangent line was drawn along the distal (posterior) border of the ramus and condyle. The angle formed at the intersection of these two lines at the gonial region was automatically calculated by the software. Measurements were recorded separately for the right and left sides. To minimize observer bias, all measurements were performed by a single calibrated examiner experienced in radiographic analysis. For intra-observer reliability assessment, 20% of the radiographs were re-evaluated after a two-week interval, and consistency of measurements was verified (reliability statistics to be inserted if calculated). The mean of the right and left gonial angle values was calculated for each subject for subsequent statistical analysis. Statistical analysis Statistical analysis was performed using SPSS Statistics version 16.0 (SPSS Inc., Released 2007, SPSS for Windows, Version 16.0, Chicago, IL, USA). Since the data distribution did not meet parametric assumptions, non-parametric analysis was applied. The Mann–Whitney U test was used to compare gonial angle values between males and females. Statistical significance was set at p < 0.05. To evaluate the diagnostic performance of the gonial angle in gender determination, Receiver Operating Characteristic (ROC) curve analysis was performed. The area under the curve (AUC), optimal cut-off value, sensitivity, and specificity were calculated. Further, binary logistic regression analysis was conducted to assess the predictive value of the gonial angle in determining gender and to estimate the probability of correct classification. All results were tabulated and interpreted at a 95% confidence interval. RESULTS A total of 158 digital panoramic radiographs were analyzed, comprising 79 males and 79 females aged between 20 and 30 years. The descriptive statistics of the gonial angle among male and female subjects are presented in Table 1 . The mean gonial angle in males was 127.78 ± 9.15 degrees, whereas in females it was 125.42 ± 6.21 degrees. The median gonial angle was 127.75 in males and 125.51 in females. The minimum and maximum values observed in males were 106.18 and 186.11 degrees, respectively. In females, the minimum and maximum values were 107.26 and 140.22 degrees, respectively. The interquartile range (IQR) was 10.89 for males and 9.03 for females. Comparison of gonial angle values between males and females using the Mann–Whitney U test did not demonstrate a statistically significant difference (p = 0.059) shown in Table 1 . Table 1 Descriptive statistics of gonial angle of male and female. Parameter Male Female p- value :0.059 Mean ± SD 127.78 ± 9.15 125.42 ± 6.21 Median 127.75 125.51 Minimum 106.18 107.26 Maximum 186.11 140.22 Median (IQR) 127.75(10.89) 125.51(9.03) Receiver Operating Characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the gonial angle in gender prediction. The area under the curve (AUC) was 0.413 with a standard error of 0.045. The 95% confidence interval ranged from 0.324 to 0.502 shown in Table 2 . Table 2 Receiver operating characteristics (ROC) analysis of gonial angle for gender prediction Test Variable AUC(Area) Std.Error 95% CI (lower-upper) Gonial angle 0.413 0.045 0.324–0.502 Table 3 Optimal cut off values of gonial angle for gender prediction based on ROC analysis Variable Suggested Cut off Sensitivity Specificity Gonial angle 125.48° 50.6% 40.5% Binary logistic regression analysis was conducted to assess the predictive value of the gonial angle for gender determination. The regression coefficient (B) for gonial angle was − 0.042 with a standard error of 0.025. The Wald statistic was 2.823, and the association was not statistically significant (p = 0.093). The adjusted odds ratio (Exp B) was 0.959, with a 95% confidence interval ranging from 0.912 to 1.007 shown in Table 4 . The classification performance of the logistic regression model is presented in Table 5 . Table 4 Binary Logistic Regression Analysis for prediction of gender Variable B S.E Wald p-value Adjusted OR (Exp B) 95% CI for OR Gonial angle (GA) -0.042 0.025 2.823 0.093 0.959 0.912–1.007 The logistic regression model derived for gender prediction was: Logit(p) = 7.781 − 0.042(GA)Logit(p) = 7.781 − 0.042(GA) where p represents the probability of being female. The probability was calculated using the equation: p = 11 + e−Logit(p)p = 1 + e−Logit(p)1​ Using a probability threshold of 0.5, values ≥ 0.5 were classified as female and values < 0.5 were classified as male. Table 5 Accuracy of prediction Accuracy of prediction Predicted Male Predicted Female %Correct Male 40 39 50.6% Female 30 49 62% Overall accuracy 56.3% Among males, 40 out of 79 were correctly classified (50.6%). Among females, 49 out of 79 were correctly classified (62%). The overall predictive accuracy of the model was 56.3%. The sensitivity (correct identification of females) was 62%, and the specificity (correct identification of males) was 50.6%. DISCUSSION Sex estimation forms a critical component of forensic identification, and the mandible has been widely investigated due to its durability and morphological variability (Farrera et al., 2025 ). The present study evaluated the gonial angle as a potential gender determinant in a Tamil Nadu population and found slightly higher mean values in males compared to females; however, the difference did not reach statistical significance (p = 0.059). Similar findings have been reported in certain Indian population-based studies demonstrating minimal sexual dimorphism in gonial angle measurements, while others have reported statistically significant differences( Website , no date). Such variability across studies may be attributed to ethnic diversity, genetic background, environmental influences, and dietary patterns affecting craniofacial growth(Acharya, Bhowmik and Naikmasur, 2014 ). Although minor mean differences were observed, ROC analysis revealed poor discriminatory performance (AUC = 0.413), indicating that gonial angle alone lacks sufficient predictive strength for gender classification. The optimal cut-off value demonstrated modest sensitivity and low specificity, and logistic regression analysis did not identify gonial angle as a statistically significant predictor (p = 0.093). These findings suggest substantial overlap in gonial angle distribution between males and females, limiting its standalone forensic applicability. Comparable observations have been documented in studies where single morphometric parameters showed inadequate classification accuracy when analyzed independently(Duan et al., 2025 ; Zahid et al., 2025 ). The biological basis of mandibular sexual dimorphism is often linked to androgen-driven muscle hypertrophy and differences in masticatory forces influencing mandibular remodeling (Lam, Pearson and Smith, 1996 ). However, the degree to which these factors translate into measurable angular differences appears population dependent(Decoste et al., 2026 ). The present findings emphasize that gonial angle, when used in isolation, demonstrates limited reliability for sex determination in the studied Tamil Nadu population. Its forensic utility may therefore be enhanced when incorporated into multivariate models alongside additional mandibular parameters rather than functioning as an independent determinant(Mączka et al., 2022 ). LIMITATIONS This study was conducted in a single institutional setting and evaluated only one mandibular parameter, thereby limiting multivariate assessment. The use of two-dimensional panoramic radiographs may introduce minimal projection-related distortion despite standardized positioning. Additionally, restriction to the 20–30 year age group, although minimizing age-related confounding factors, may limit generalizability to broader populations. CONCLUSION Within the limitations of the present study, the gonial angle demonstrated limited reliability as an independent indicator for gender determination in the Tamil Nadu population. Although slight differences were observed between males and females, significant overlap reduced its discriminatory power. The gonial angle should therefore not be used as a standalone forensic marker but may serve as an adjunctive parameter within multivariate predictive models. Population-specific validation remains essential before medico-legal application. Abbreviations OPG Orthopantomograms SPSS Statistical Package for Social Sciences ROC Receiver Operating Characteristic AUC Area Under the Curve IQR InterQuartile Range Declarations Ethics approval and consent to participate - Institutional ethical clearance was obtained prior to commencement of the study (IHEC/SDC/UG-2289/26/FORENSIC/081). Informed consent was obtained from participants who were willing to participate in the study. Consent for publication - Consent to use the radiographs for scientific research and publication was obtained from the participants prior to the start of the study. Funding - Nil Author Contribution BINIGHA. M: Literature search, manuscripts draftingDr. Abirami Arthanari: Data collection analysis, Data verification, manuscripts drafting References Acharya AB, Bhowmik B, Naikmasur VG (2014) Accuracy of identifying juvenile/adult status from third molar development using prediction probabilities derived from logistic regression analysis. J Forensic Sci 59(3):665–670 Adnan A et al (2025) ‘Developmental validation of the SF 28CS typing system: a robust 6-dye multiplex for forensic human identification’, International journal of legal medicine [Preprint]. Available at: https://doi.org/10.1007/s00414-025-03653-5 Craig GG, Powell KR, Price CA (2013) Clinical evaluation of a modified silver fluoride application technique designed to facilitate lesion assessment in outreach programs. BMC Oral Health 13:73 Decoste J et al (2026) ‘The role of sagittal maxillary-mandibular relationships on perceptions of facial shape esthetics: A three-dimensional morphometric analysis’, American journal of orthodontics and dentofacial orthopedics: official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics [Preprint]. Available at: https://doi.org/10.1016/j.ajodo.2025.11.015 Duan Y et al (2025) Machine learning-based risk stratification for gastrointestinal bleeding in ICU patients with cirrhosis: evidence from the MIMIC database. Front Med 12:1701973 Farrera A et al (2025) Bridging research and practice: A critical review of biological profile estimation methods applied to the Mexican population. Sci justice: J Forensic Sci Soc 65(4):101260 Kazama RN et al (2025) ‘Sex Identification Using Computed Tomography Images of the Human Skull’, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference , 2025, pp. 1–7 Koenig C et al (2024) Automated High-Throughput Biological Sex Identification from Archeological Human Dental Enamel Using Targeted Proteomics. J Proteome Res 23(11):5107–5121 Lam YM, Pearson OM, Smith CM (1996) Chin morphology and sexual dimorphism in the fossil hominid mandible sample from Klasies River Mouth. Am J Phys Anthropol 100(4):545–557 Lynch V, Heathfield LJ, Budowle B (2025) Disclosure of biological sex may impact individual privacy. Forensic SciInt Genet 76:103213 Mączka G et al (2022) Morphology of the antegonial notch and its utility in the determination of sex on skeletal materials. J Anat 241(4):919–927 Ndjonko LCM et al (2026) Evaluating predictive performance and generalizability of traditional and artificial intelligence models in predicting surgical site infections postspinal surgery: a systematic review. spine journal: official J North Am Spine Soc 26(2):280–291 Pinto C et al (2025) The estimation of biological sex through the radius and ulna in Portuguese reference skeletal samples. Int J Legal Med 139(5):2403–2412 Sisman Y et al (2012) Radiographic evaluation on prevalence of Stafne bone defect: a study from two centres in Turkey. Dento Maxillo Fac Radiol 41(2):152–158 Alfawzan AA (2020) Gonial Angle as a Determinant of Gender, a Panoramic Study in a Sample of Saudi Population. IJPHRD 11(1):1689–1693 Zahid MA et al (2025) Assessing the diagnostic accuracy of biochemical, anthropometric, and combined indices for metabolic syndrome prediction in a cohort from Qatar Biobank. PLoS ONE 20(12):e0339340 Additional Declarations No competing interests reported. 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Diagnostic Evaluation of the Gonial Angle for Gender Determination - An Anthropometric Approach","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSex determination forms the cornerstone of forensic identification and significantly narrows the biological profile of unidentified remains(Lynch, Heathfield and Budowle, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In scenarios involving mass disasters, explosions, or advanced decomposition, skeletal structures often represent the primary source of identification. International forensic identification protocols, including those endorsed by Interpol, emphasize the value of durable skeletal markers exhibiting sexual dimorphism (Koenig et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe mandible is the strongest and most resistant bone of the facial skeleton and frequently survives environmental and mechanical insults (Koenig et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Adnan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Morphological differences between males and females arise due to variations in growth patterns, hormonal influences, and masticatory loading. Several mandibular parameters including ramus height, bigonial width, body length, and gonial angle have been investigated for their forensic relevance (Kazama et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe gonial angle, formed by the intersection of the inferior border of the mandibular body and the posterior border of the ramus, is easily identifiable on panoramic radiographs. Despite numerous investigations, findings regarding its sexual dimorphism remain inconsistent across populations. Ethnic variability, genetic diversity, dietary habits, and biomechanical forces may contribute to these discrepancies(Farrera et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Pinto et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eDigital orthopantomography provides a non-invasive and accessible method for assessing mandibular morphology (Craig, Powell and Price, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). While three-dimensional imaging offers enhanced accuracy, panoramic radiography remains widely used in clinical and forensic settings due to feasibility and availability (Sisman et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Although many studies report statistically significant gender differences in gonial angle, fewer have evaluated its predictive accuracy using analytical models such as ROC curves and logistic regression(Ndjonko et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to assess the reliability and predictive accuracy of gonial angle for gender determination in individuals aged 20\u0026ndash;30 years from south indian population using digital panoramic radiographs.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThe present retrospective cross-sectional study was conducted in the Department of Forensic Odontology. Digital panoramic radiographs were obtained from the archives of the Department of Oral Medicine and Radiology, Saveetha Dental College and Hospital. Institutional ethical clearance was obtained prior to commencement of the study (IHEC/SDC/UG-2289/26/FORENSIC/081).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy sample\u003c/h3\u003e\n\u003cp\u003eThe study comprised a total of 158 digital orthopantomograms (OPGs), including 79 males and 79 females. The age of the subjects ranged from 20 to 30 years. The age group was selected to minimize age-related morphological changes in the mandible and to ensure skeletal maturity.\u003c/p\u003e \u003cp\u003eRadiographs were selected using a non-probability consecutive sampling method from archived records. The sex of each individual was confirmed from institutional records prior to inclusion.\u003c/p\u003e\n\u003ch3\u003eInclusion criteria\u003c/h3\u003e\n\u003cp\u003eRadiographs were included in the study if they met the following criteria:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eRadiographs of individuals aged between 20 and 30 years\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRadiographs with documented sex\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHigh-quality digital panoramic radiographs with clear visualization of bilateral mandibular borders\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRadiographs free from distortion affecting the gonial region\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eExclusion criteria\u003c/h3\u003e\n\u003cp\u003eRadiographs were excluded under the following conditions:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePresence of pathological lesions involving the mandible\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHistory or radiographic evidence of mandibular fractures\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDevelopmental anomalies of the mandible\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRadiographs exhibiting positioning errors, magnification distortion, or unclear visualization of the gonial region\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRadiographs with abnormalities that interfered with accurate identification of anatomical landmarks\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eRadiographic procedure\u003c/h3\u003e\n\u003cp\u003eAll the panoramic radiographic images were acquired using Planmeca ProMax\u0026reg; 3D Max panoramic machine (Planmeca Oy, Helsinki, Finland). Exposure parameters were\u003c/p\u003e \u003cp\u003eranging from 64\u0026ndash;68 kVp, 6.3\u0026ndash;10 mA, and 0.19 s according to the patient\u0026rsquo;s age and size. The radiographs had been taken following standard exposure protocols with patients positioned according to manufacturer guidelines to ensure reproducibility and minimize distortion. Only radiographs meeting institutional quality assurance standards were considered for analysis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of gonial angle\u003c/h2\u003e \u003cp\u003eThe gonial angle was measured bilaterally using Planmeca Romexis software (Planmeca Oy, Helsinki, Finland), which allows digital tracing and angular measurement with precision tools.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLandmark identification\u003c/h3\u003e\n\u003cp\u003eThe gonial angle was defined as the angle formed by the intersection of:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eA line tangent to the inferior border of the mandibular body\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eA line tangent to the posterior border of the ascending ramus and condylar process which shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eMeasurement procedure\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe digital image was opened in the software measurement interface.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe first tangent line was drawn along the lower border of the mandible, extending posteriorly.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe second tangent line was drawn along the distal (posterior) border of the ramus and condyle.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe angle formed at the intersection of these two lines at the gonial region was automatically calculated by the software.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMeasurements were recorded separately for the right and left sides.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTo minimize observer bias, all measurements were performed by a single calibrated examiner experienced in radiographic analysis. For intra-observer reliability assessment, 20% of the radiographs were re-evaluated after a two-week interval, and consistency of measurements was verified (reliability statistics to be inserted if calculated).\u003c/p\u003e \u003cp\u003eThe mean of the right and left gonial angle values was calculated for each subject for subsequent statistical analysis.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS Statistics version 16.0 (SPSS Inc., Released 2007, SPSS for Windows, Version 16.0, Chicago, IL, USA). Since the data distribution did not meet parametric assumptions, non-parametric analysis was applied. The Mann\u0026ndash;Whitney U test was used to compare gonial angle values between males and females. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. To evaluate the diagnostic performance of the gonial angle in gender determination, Receiver Operating Characteristic (ROC) curve analysis was performed. The area under the curve (AUC), optimal cut-off value, sensitivity, and specificity were calculated.\u003c/p\u003e \u003cp\u003eFurther, binary logistic regression analysis was conducted to assess the predictive value of the gonial angle in determining gender and to estimate the probability of correct classification. All results were tabulated and interpreted at a 95% confidence interval.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 158 digital panoramic radiographs were analyzed, comprising 79 males and 79 females aged between 20 and 30 years. The descriptive statistics of the gonial angle among male and female subjects are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean gonial angle in males was 127.78\u0026thinsp;\u0026plusmn;\u0026thinsp;9.15 degrees, whereas in females it was 125.42\u0026thinsp;\u0026plusmn;\u0026thinsp;6.21 degrees. The median gonial angle was 127.75 in males and 125.51 in females. The minimum and maximum values observed in males were 106.18 and 186.11 degrees, respectively. In females, the minimum and maximum values were 107.26 and 140.22 degrees, respectively. The interquartile range (IQR) was 10.89 for males and 9.03 for females. Comparison of gonial angle values between males and females using the Mann\u0026ndash;Whitney U test did not demonstrate a statistically significant difference (p\u0026thinsp;=\u0026thinsp;0.059) shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of gonial angle of male and female.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ep- value :0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127.78\u0026thinsp;\u0026plusmn;\u0026thinsp;9.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.42\u0026thinsp;\u0026plusmn;\u0026thinsp;6.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127.75(10.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.51(9.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eReceiver Operating Characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the gonial angle in gender prediction. The area under the curve (AUC) was 0.413 with a standard error of 0.045. The 95% confidence interval ranged from 0.324 to 0.502 shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReceiver operating characteristics (ROC) analysis of gonial angle for gender prediction\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC(Area)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI (lower-upper)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGonial angle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.324\u0026ndash;0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOptimal cut off values of gonial angle for gender prediction based on ROC analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuggested Cut off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGonial angle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125.48\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBinary logistic regression analysis was conducted to assess the predictive value of the gonial angle for gender determination. The regression coefficient (B) for gonial angle was \u0026minus;\u0026thinsp;0.042 with a standard error of 0.025. The Wald statistic was 2.823, and the association was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.093). The adjusted odds ratio (Exp B) was 0.959, with a 95% confidence interval ranging from 0.912 to 1.007 shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The classification performance of the logistic regression model is presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinary Logistic Regression Analysis for prediction of gender\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted OR (Exp B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI for OR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGonial angle (GA)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.912\u0026ndash;1.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe logistic regression model derived for gender prediction was:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eLogit(p)\u0026thinsp;=\u0026thinsp;7.781\u0026thinsp;\u0026minus;\u0026thinsp;0.042(GA)Logit(p)\u0026thinsp;=\u0026thinsp;7.781\u0026thinsp;\u0026minus;\u0026thinsp;0.042(GA)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ewhere \u003cem\u003ep\u003c/em\u003e represents the probability of being female. The probability was calculated using the equation:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ep\u0026thinsp;=\u0026thinsp;11\u0026thinsp;+\u0026thinsp;e\u0026minus;Logit(p)p\u0026thinsp;=\u0026thinsp;1\u0026thinsp;+\u0026thinsp;e\u0026minus;Logit(p)1​\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eUsing a probability threshold of 0.5, values\u0026thinsp;\u0026ge;\u0026thinsp;0.5 were classified as female and values\u0026thinsp;\u0026lt;\u0026thinsp;0.5 were classified as male.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAccuracy of prediction\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccuracy of prediction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredicted Male\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePredicted Female\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%Correct\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall accuracy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAmong males, 40 out of 79 were correctly classified (50.6%). Among females, 49 out of 79 were correctly classified (62%). The overall predictive accuracy of the model was 56.3%. The sensitivity (correct identification of females) was 62%, and the specificity (correct identification of males) was 50.6%.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eSex estimation forms a critical component of forensic identification, and the mandible has been widely investigated due to its durability and morphological variability (Farrera et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The present study evaluated the gonial angle as a potential gender determinant in a Tamil Nadu population and found slightly higher mean values in males compared to females; however, the difference did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.059). Similar findings have been reported in certain Indian population-based studies demonstrating minimal sexual dimorphism in gonial angle measurements, while others have reported statistically significant differences(\u003cem\u003eWebsite\u003c/em\u003e, no date). Such variability across studies may be attributed to ethnic diversity, genetic background, environmental influences, and dietary patterns affecting craniofacial growth(Acharya, Bhowmik and Naikmasur, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough minor mean differences were observed, ROC analysis revealed poor discriminatory performance (AUC\u0026thinsp;=\u0026thinsp;0.413), indicating that gonial angle alone lacks sufficient predictive strength for gender classification. The optimal cut-off value demonstrated modest sensitivity and low specificity, and logistic regression analysis did not identify gonial angle as a statistically significant predictor (p\u0026thinsp;=\u0026thinsp;0.093). These findings suggest substantial overlap in gonial angle distribution between males and females, limiting its standalone forensic applicability. Comparable observations have been documented in studies where single morphometric parameters showed inadequate classification accuracy when analyzed independently(Duan et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zahid et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe biological basis of mandibular sexual dimorphism is often linked to androgen-driven muscle hypertrophy and differences in masticatory forces influencing mandibular remodeling (Lam, Pearson and Smith, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). However, the degree to which these factors translate into measurable angular differences appears population dependent(Decoste et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The present findings emphasize that gonial angle, when used in isolation, demonstrates limited reliability for sex determination in the studied Tamil Nadu population. Its forensic utility may therefore be enhanced when incorporated into multivariate models alongside additional mandibular parameters rather than functioning as an independent determinant(Mączka et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLIMITATIONS\u003c/h2\u003e \u003cp\u003eThis study was conducted in a single institutional setting and evaluated only one mandibular parameter, thereby limiting multivariate assessment. The use of two-dimensional panoramic radiographs may introduce minimal projection-related distortion despite standardized positioning. Additionally, restriction to the 20\u0026ndash;30 year age group, although minimizing age-related confounding factors, may limit generalizability to broader populations.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eWithin the limitations of the present study, the gonial angle demonstrated limited reliability as an independent indicator for gender determination in the Tamil Nadu population. Although slight differences were observed between males and females, significant overlap reduced its discriminatory power. The gonial angle should therefore not be used as a standalone forensic marker but may serve as an adjunctive parameter within multivariate predictive models. Population-specific validation remains essential before medico-legal application.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOPG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOrthopantomograms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Under the Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterQuartile Range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e- Institutional ethical clearance was obtained prior to commencement of the study (IHEC/SDC/UG-2289/26/FORENSIC/081). Informed consent was obtained from participants who were willing to participate in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e- Consent to use the radiographs for scientific research and publication was obtained from the participants prior to the start of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e - Nil\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBINIGHA. M: Literature search, manuscripts draftingDr. Abirami Arthanari: Data collection analysis, Data verification, manuscripts drafting\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcharya AB, Bhowmik B, Naikmasur VG (2014) Accuracy of identifying juvenile/adult status from third molar development using prediction probabilities derived from logistic regression analysis. J Forensic Sci 59(3):665\u0026ndash;670\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdnan A et al (2025) \u0026lsquo;Developmental validation of the SF 28CS typing system: a robust 6-dye multiplex for forensic human identification\u0026rsquo;, \u003cem\u003eInternational journal of legal medicine\u003c/em\u003e [Preprint]. Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00414-025-03653-5\u003c/span\u003e\u003cspan address=\"10.1007/s00414-025-03653-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCraig GG, Powell KR, Price CA (2013) Clinical evaluation of a modified silver fluoride application technique designed to facilitate lesion assessment in outreach programs. BMC Oral Health 13:73\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDecoste J et al (2026) \u0026lsquo;The role of sagittal maxillary-mandibular relationships on perceptions of facial shape esthetics: A three-dimensional morphometric analysis\u0026rsquo;, \u003cem\u003eAmerican journal of orthodontics and dentofacial orthopedics: official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics\u003c/em\u003e [Preprint]. Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ajodo.2025.11.015\u003c/span\u003e\u003cspan address=\"10.1016/j.ajodo.2025.11.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuan Y et al (2025) Machine learning-based risk stratification for gastrointestinal bleeding in ICU patients with cirrhosis: evidence from the MIMIC database. Front Med 12:1701973\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarrera A et al (2025) Bridging research and practice: A critical review of biological profile estimation methods applied to the Mexican population. Sci justice: J Forensic Sci Soc 65(4):101260\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKazama RN et al (2025) \u0026lsquo;Sex Identification Using Computed Tomography Images of the Human Skull\u0026rsquo;, \u003cem\u003eAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference\u003c/em\u003e, 2025, pp. 1\u0026ndash;7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoenig C et al (2024) Automated High-Throughput Biological Sex Identification from Archeological Human Dental Enamel Using Targeted Proteomics. J Proteome Res 23(11):5107\u0026ndash;5121\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLam YM, Pearson OM, Smith CM (1996) Chin morphology and sexual dimorphism in the fossil hominid mandible sample from Klasies River Mouth. Am J Phys Anthropol 100(4):545\u0026ndash;557\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLynch V, Heathfield LJ, Budowle B (2025) Disclosure of biological sex may impact individual privacy. Forensic SciInt Genet 76:103213\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMączka G et al (2022) Morphology of the antegonial notch and its utility in the determination of sex on skeletal materials. J Anat 241(4):919\u0026ndash;927\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNdjonko LCM et al (2026) Evaluating predictive performance and generalizability of traditional and artificial intelligence models in predicting surgical site infections postspinal surgery: a systematic review. spine journal: official J North Am Spine Soc 26(2):280\u0026ndash;291\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinto C et al (2025) The estimation of biological sex through the radius and ulna in Portuguese reference skeletal samples. Int J Legal Med 139(5):2403\u0026ndash;2412\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSisman Y et al (2012) Radiographic evaluation on prevalence of Stafne bone defect: a study from two centres in Turkey. Dento Maxillo Fac Radiol 41(2):152\u0026ndash;158\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlfawzan AA (2020) Gonial Angle as a Determinant of Gender, a Panoramic Study in a Sample of Saudi Population. IJPHRD 11(1):1689\u0026ndash;1693\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZahid MA et al (2025) Assessing the diagnostic accuracy of biochemical, anthropometric, and combined indices for metabolic syndrome prediction in a cohort from Qatar Biobank. PLoS ONE 20(12):e0339340\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gonial angle, Gender determination, Orthopantomogram, Sexual dimorphism, Forensic odontology","lastPublishedDoi":"10.21203/rs.3.rs-9175757/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9175757/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSex estimation is a critical step in forensic human identification. The mandible, owing to its structural resilience and morphological variation between sexes, has been widely studied for gender determination. Among its parameters, the gonial angle has been proposed as a potential radiomorphometric indicator; however, population-specific validation remains limited in the Tamil Nadu population.\u003c/p\u003e\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eTo evaluate the reliability and predictive accuracy of the gonial angle for gender determination using digital orthopantomograms in a South Indian population.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eA retrospective cross-sectional study was conducted on 158 digital panoramic radiographs comprising 79 males and 79 females aged 20\u0026ndash;30 years. Bilateral gonial angles were measured using Planmeca Romexis software. Gender differences were assessed using the Mann\u0026ndash;Whitney U test. Diagnostic performance was evaluated using Receiver Operating Characteristic (ROC) curve analysis, and binary logistic regression analysis was performed to assess predictive accuracy. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean gonial angle was 127.78\u0026deg; \u0026plusmn; 9.15 in males and 125.42\u0026deg; \u0026plusmn; 6.21 in females. The difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.059). ROC analysis demonstrated poor discriminatory ability (AUC\u0026thinsp;=\u0026thinsp;0.413; 95% CI: 0.324\u0026ndash;0.502). The optimal cut-off value (125.48\u0026deg;) yielded sensitivity of 50.6% and specificity of 40.5%. Logistic regression analysis did not show gonial angle to be a significant predictor of gender (p\u0026thinsp;=\u0026thinsp;0.093). Overall classification accuracy was 56.3%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWithin the studied Tamil Nadu population, gonial angle alone demonstrated limited predictive value for gender determination. It may serve as an adjunctive parameter when combined with other mandibular measurements in multivariate models.\u003c/p\u003e","manuscriptTitle":"Sexual Dimorphism or Statistical Overlap? Diagnostic Evaluation of the Gonial Angle for Gender Determination - An Anthropometric Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-15 19:15:40","doi":"10.21203/rs.3.rs-9175757/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dc7666cd-f1f1-4ccb-b695-9f32d38e946d","owner":[],"postedDate":"April 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T07:26:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-15 19:15:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9175757","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9175757","identity":"rs-9175757","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}